Neuroscientific questions

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tion by successfully capturing and modeling more detailed diffusion profiles.

Furthermore, HARDI-based tractography approaches (Part III) provide a more precise approximation of fiber pathways. Fiber reconstructions emerging from defined cortical regions help neuroscientists to understand which brain regions are related to certain tasks and in addition, which cortical zones are involved.

For example in transcranial magnetic stimulation (TMS) studies, cortical regions related to certain tasks are determined and used as a region of interest for trac-tography. TMS provides the unique possibility to achieve immediate feedback when blocking certain cortical areas by inducing electric current. As a result, brain regions, which are involved with certain tasks, are identified individually.

More important, their impact on certain tasks is examined in the course of an TMS experiment. Figure 2.8 shows the setup of an TMS experiment.

Figure 2.8: Setup of a TMS experiment related to the study; undertaken by Han-nula et al. [40] at the BioMag Laboratory at Helsinki University Central Hos-pital using the Nextim [71] TMS navigation system: The subject sitting in the chair wears marker equipped glasses in order to localize the head and match it with preoperatively acquired data, such as DTI. Reflecting markers are added to the coil in order to localize it and perform blocking or stimula-tion with high precision.

In terms of understanding brain functioning, TMS offers a huge contribution:

Task-related cortical zones are not only identified but also triggered by stimula-tion; as such, their impact is examined with immediate feedback. In the course of an TMS experiment, scientists examine the behavior of a subject perform-ing a specific task while they blocked or stimulated involved cortical activation zones. The inclusion of a previously acquired diffusion dataset also facilitates task-related tractography by using a seed region for fiber reconstruction, which is defined by the TMS target region. Therefore, further white matter connec-tions lead to secondary activation areas being additionally involved in the spe-cific task. In a subsequent step these areas can be subject to TMS stimulation or blocking in order to examine their influence on the task. HARDI tractography approaches have further potential to enhance research in this field since hypoth-esis concerning task-involved cortical activation areas include small pathways.

As a result of HARDI tract reconstruction, more intricate and small fiber config-urations can be examined. A TMS study including DTI was performed by Han-nula et al. [40]. The authors successfully reconstructed the connection between theprimary somatic sensory cortex (S1) and themiddle frontal gyrus(MFG). In ad-dition they could corroborate their hypothesis that a single TMS pulse to the MFG plays an important role in tactile-related working memory performance.

Furthermore, a combination of diffusion imaging and functional magnetic resonance imaging (fMRI) is promising. fMRI reveals task-related activation zones by measuring the blood oxygenation level in the brain when performing a specific task such as finger tapping. A multimodal analysis approach, using acquired fMRI hot spots, directly relates white matter tracts as the anatomical connection of activation areas (Section 9.5). Therefore, both TMS- and fMRI-related tract reconstructions lead to anatomical as well as functional meaning-ful information about white matter connectivity which is beneficial in terms of fundamental neuroscience and neurosurgical planning.

It has been reported that the degree of diffusion anisotropy is related to age and gender. Myelinization is the process of myelin growing around axons, which acts as an insulating membrane that facilitates the conduction of nerve impulses. For this reason, myelinated fiber tracts comprise a higher anisotropy than non-myelinated ones. In fact, the degree of fiber-myelinization is related

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to the maturity of the brain: Newborns show less myelin around fiber tracts.

Consequently, brain development is examined using diffusion imaging and dif-fusion classifiers, showing the integrity of fiber pathways (Section 4.5 and 4.6).

On the other hand, an age-related loss of regional white matter is reported. This anatomical change results in decreased anisotropic diffusion. Diffusion imag-ing provides quantitative data by analyzimag-ing the diffusion probability function using diffusion anisotropy classifiers. As a result, diffusion imaging is used to monitor both brain development and aging.

2.5.2 Neurological disorders

Since diffusion imaging measures the degree of free diffusion, quantitative anal-ysis of regions is also feasible. Differences in white matter diffusion profiles indicate abnormalities in terms of tract location or integrity.

Multiple sclerosis(MS) is a chronic inflammatory demyelinating disease, fea-turing a relapsing-remitting course in which symptoms emerge and improve over a certain period. Studies have shown that MS not only causes demyeli-nation but also axonal damage. This leads to a white matter directionality loss in the diffusion profile which is detected by diffusion imaging. Using tract re-constructions in combination with anisotropy classifiers (Section 7) or regional anisotropy examination (Section 4.5 and 4.6) is helpful in diagnosing, under-standing, and monitoring MS.

In terms of neurodegenerative diseases such asmild cognitive impairment(MCI) orAlzheimer’s disease (AD), diffusion imaging provides important information:

Anisotropy classifiers, ODF-based fiber visualization (Section 6) or whole bun-dle visualization (Section 7) are beneficial in order to predict type, location, as well as timing of tissue degeneration. For example, MCI has been proven to be a precursor of AD; therefore an early diagnosis of MCI is vital in terms of iden-tifying white matter degeneration and examining the development of AD. In addition, detailed diffusion analysis is beneficial in staging neurodegenerative diseases and monitoring the progress.

2.5.3 Neurosurgical planning

Brain lesions, such as tumors, are one of the most common abnormalities in the brain. Essential brain properties such as functional zones located on the cortex and white matter tracts connecting these are considered as risk structures in sur-gical interventions. In fact, damage to both activation zones as well as neuronal fibers, can lead to severe postoperative impairment. Therefore, the aim in neu-rosurgical planning is the minimization of postoperative damages. Nowadays, neurosurgery is feasible for deep-seated lesions or even lesions located close to essential white matter tracts. This is due to sophisticated MRI technology such as fMRI and diffusion imaging. Preoperatively, essential information about the spatial relationship between the lesion and vital brain structures is obtained through imaging and visualization. Challenging neurosurgery is preceded by a planning phase in which multiple volumes are combined (Section 9.5) and risk structures as well as safest access paths are defined (Section 9.6).

One of the leading clinical questions in terms of neurosurgical planning and pathology examination is whether a white matter tract is displaced or infil-trated by a lesion. Combining diffusion classifiers and tractography approaches within one visual representation (Section 6 and Section 7) provides vital infor-mation for answering this question. Fiber characteristics such as integrity in-formation are directly visualized on the bundle hull and are thereby combined with information about the spatial position of the lesion.